New Version of Gurucul Risk Analytics Provides Anomaly and Risk
Detection across Network, Mobile, IoT, Medical Devices and More

LOS ANGELES–(BUSINESS WIRE)–lt;a href=”https://twitter.com/hashtag/EY?src=hash” target=”_blank”gt;#EYlt;/agt;–Gurucul, a leader in behavior based
security and fraud analytics technology, today announced a new version
of its Gurucul Risk Analytics (GRA) platform, which extends behavior
based security analytics with pre-built machine learning models that
span the entire IT stack. GRA version 7.0 unifies siloed analytics to
provide real-time anomaly and risk detection across enterprise and cloud
platforms/applications, networks, mobile endpoints, IoT devices, medical
devices, business applications and more. Gurucul goes beyond SIEM’s
capabilities, including the ability to automate security controls such
as risk and behavior based step-up authentication and preventative DLP
enforcement in high risk situations.

Gurucul will demonstrate the new GRA platform at RSA Conference 2019
Booth #2027 in the South Expo Hall from March 4-7.

The new version of GRA also provides a new streamlined user experience
that includes an open and flexible framework for personalizing
widget-driven dashboards with a wide range of visualizations and
canvas-based components to view, modify or build new behavior and threat
models using Gurucul Studio™. Gurucul GRA is available as a cloud
service, and can be deployed in the cloud, on-premises data centers, or
hybrid environments.

Digital transformation is expanding the traditional enterprise attack
surface to include a variety of new devices that are interconnected and
use off the shelf operating systems including IoT devices, medical
equipment, POS systems, etc. Detecting malicious activity in these
distributed and traffic intensive environments is beyond the
capabilities of siloed, rule and pattern-based monitoring solutions.
Gurucul offers a real-time behavior analytics platform that uses open
choice, “no cost” Big Data to collect high-frequency events /
transactions and contextual metadata from the entire IT stack and run
machine learning models that detect and risk-score suspicious activity.

“For effective risk mitigation, a security analytics platform must be
able to span the entire IT footprint of an organization and provide an
open framework to create user defined entities, modify existing machine
learning models and trigger risk-response actions in real-time,” said
Nilesh Dherange, CTO of Gurucul. “Just as we were the first to extend
behavior analytics from on-premises to the Cloud, Gurucul UEBA is the
only solution helping customers with risk detection and scoring to the
extended enterprise of mobile, IoT, PoS, medical and other entities.”

Custom Dashboards & Visualization

To address specific business functions and use case requirements,
Gurucul Risk Analytics now provides out-of-the-box dashboards for UEBA,
fraud analytics, cloud analytics, access analytics, network analytics,
as well as customizable business roles including SOC Analyst, Network
Analyst, DLP Analyst, Privacy Officer, Data Scientist, etc. Each
dashboard can be easily customized using drag and drop widgets to
provide data and visualizations tailored to each user’s needs and
preferences.

Largest ML Model Library and Open Analytics Framework

To detect advanced threats from external attackers and malicious
insiders such as fraud, data exfiltration, and account compromise,
Gurucul now has more than 1000 pre-packaged machine learning models.
These include unsupervised, supervised and deep learning algorithms, as
well as versions that are pre-tuned to predict and detect specific types
of threats and for industry use cases such as finance, healthcare and
retail.

In addition, organizations can easily customize existing models or build
their own using Gurucul STUDIO, which provides canvas-based
drag-and-drop components for analysts, data scientists or administrators
to design behavior, threat and risk models without having to write code.
STUDIO also provides a centralized analytics platform and SDKs for data
scientists to build and import their own custom models.

Gurucul’s vast library of ML models also enables organizations to
implement model-driven security to automate responses to high risk
activity and reduce security “friction”. For example, powered by ML
models, behavioral risk based authentication can improve the end user
experience by doing away with passwords while increasing security. This
continuous, model-driven authentication process can make in-the-moment
decisions about a users’ confirmed identity before allowing the session
or requested action to continue. Authentication and authorization are no
longer a singular event, but an engaged process that persists throughout
the user’s experience in the environment.

Availability

Gurucul Risk Analytics 7.0 is available immediately from Gurucul and its
business partners worldwide as a subscription-based cloud service,
software for cloud, on-premise or hybrid environment deployment, and as
a managed service from Gurucul Labs™.

About Gurucul

Gurucul is a global cyber security company that is changing the way
organizations protect their most valuable assets, data and information
from insider and external threats both in the cloud and on-premises.
Gurucul’s real-time behavior based security analytics and intelligence
technology combines machine learning behavior profiling with predictive
risk-scoring algorithms to predict, detect and prevent breaches, fraud
and insider threats. Gurucul technology is used by Global 1000 companies
and government agencies to fight cyber fraud, IP theft and account
compromise. The company is based in Los Angeles. To learn more, visit https://gurucul.com/
and follow us on LinkedIn
and Twitter.